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Range expansion in asexual dandelions: selection for general-purpose genotypes?
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Carla Oplaat and Koen JF Verhoeven*
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Netherlands Institute of Ecology (NIOO-KNAW), Department of Terrestrial Ecology, Droevendaalsesteeg 10,
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6708 PB Wageningen, The Netherlands
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*Correspondence author. E-mail: k.verhoeven@nioo.knaw.nl
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Running title: Selection for general-purpose genotypes
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Summary
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1.
Phenotypic plasticity and broad ecological tolerance are hypothesized as important traits in the range
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expansion of asexual species, because individual asexual lineages have to face spatial and temporal
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environmental variation with limited opportunity for genetic adaptation. The hypothesis that asexual
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lineages are general-purpose genotypes (GPG) has been previously tested, with mixed results, in
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species that have both sexual and asexual variants. Such comparisons can be confounded with intra-
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specific ploidy level differences that are often observed between the two reproductive types.
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Avoiding the confounding effects of ploidy differences, we test whether northward range expansion
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selects for a GPG strategy in asexual lineages of the common dandelion (Taraxacum officinale), a
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species that shows geographic parthenogenesis. We compared the biomass of asexual lineages that
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were collected along a geographic transect from close to the asexuals’ area of origin (central Europe,
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where asexuals descend from sexual ancestors in mixed populations) towards their northern
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distribution edge (Northern Europe, where only asexual lineages occur) in three different
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experimental environments: optimal, drought and shaded conditions.
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The geometric mean performance across test environments did not differ significantly between plants
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from different regions along the transect. However, southern lineages typically showed larger
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differences in biomass between different test environments, mainly caused by a relatively high
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performance in the optimal environment. Northern and mid-latitude lineages showed more even
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performance across the different environments.
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Synthesis. Our results suggest that phenotypic plasticity is important in the asexual range expansion of
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Taraxacum officinale and that range shifting in this species is accompanied by a change in phenotypic
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plasticity strategy from lineages with high ability to increase biomass in optimal growing conditions
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(master-of-some strategy) to lineages that maintain more constant performance in different
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environments (GPG or jack-of-all-trades strategy) from core to range edge.
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Key-words: apomixis, geographic parthenogenesis, jack-of-all-trades strategy, master-of-some strategy,
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phenotypic plasticity, Taraxacum officinale (common dandelion).
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Introduction
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Phenotypic plasticity is the ability of a genotype to respond to environmental changes (Bradshaw, 1965;
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Bradshaw, 2006; Nicotra et al., 2010) and is thought to play an important role in invasions, migration and
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colonization of novel sites (Agrawal, 2001; Yeh and Price, 2004; Richards, Pennings and Donovan, 2005;
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Mozdzer and Megonigal, 2012; Skalova, Havlickova and Pysek, 2012). Two main strategies have been put
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forward in which phenotypic plasticity can contribute to successful invasions: (1) a general-purpose or jack-of-
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all-trades strategy; and (2) the master-of-some strategy (Sultan, 2001; Richards et al., 2006; Muth and Pigliucci,
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2007; Hulme, 2008). If robust fitness is the key to success, a jack-of-all-trades strategy is expected in which
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plasticity allows a relatively high and robust fitness across a broad range of environments (Baker, 1965;
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Vorburger, Sunnucks and Ward, 2003; Richards et al., 2006). In contrast, master-of-some genotypes have
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success due to their ability to rapidly take advantage of available resources in optimal conditions while
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maintaining reasonable performance in poor environments (Sultan, 2001; Richards et al., 2006).
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The importance of phenotypic plasticity during invasions and range expansion applies to a wide range of
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species, but has been emphasized specifically for strongly self-fertilizing and asexual lineages (Baker, 1965;
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Lynch, 1984) which have reduced opportunity to deal with changing environments via genetic adaptation.
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Baker (1965) proposed the general-purpose genotype (GPG) model for self-fertilizing weeds, where individual
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genotypes have a broad ecological tolerance owing to high phenotypic plasticity. In the context of geographical
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parthenogenesis, where asexuals show a wider geographical distribution compared to their close sexual
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relatives from which they derive (Bierzychudek, 1985; Jose and Dufresne, 2010; Cosendai et al., 2013), Lynch
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(1984) proposed that asexual reproduction and clonal selection favours the evolution of GPGs and phenotypic
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plasticity, since a single genotype has to face temporal variation in environments. This may predispose asexuals
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to be better colonizers of novel habitats than their sexual ancestors, thus contributing to the pattern of
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geographic parthenogenesis (Lynch, 1984). The hypothesis that GPG-selection contributes to geographic
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parthenogenesis contains two components: first, that clonal selection favours a GPG strategy, and second, that
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a GPG-strategy facilitates range expansion and colonization of new habitats. Empirical studies to date have
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mainly emphasized the first component that clonal selection favours a GPG strategy, by comparing plasticity
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and performance between asexuals and their sexual relatives. These studies have yielded mixed results. Higher
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phenotypic plasticity and/or more even performance between environments in asexuals compared to their
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sexual close relatives was observed in some systems (Michaels and Bazzaz, 1986; Bierzychudek, 1989; Haack et
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al., 2000) but not in others (Kenny, 1996; Vorburger, Sunnucks and Ward, 2003; see also review by Vrijenhoek
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and Parker, 2009). Rather than benefiting from GPG-selection, several other (non-exclusive) factors have been
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suggested to contribute to better range expansion of asexuals and therefore contribute to geographic
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parthenogenesis, including the ecological advantage of uni-parental reproduction, ploidy differences and biotic
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interactions (Horandl, 2006; Adolfsson et al., 2010; Verhoeven and Biere, 2013).
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One complicating factor in the comparison between asexuals and their sexual relatives is that the two often
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show different ploidy levels (Lively, 1987; Dybdahl and Lively, 1995; Kenny, 1996; de Kovel and de Jong, 1999;
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Stanton, Roy and Thiede, 2000; Jose and Dufresne, 2010). Genome polyploidization may result in new gene
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combinations, increased biomass and elevated levels of heterozygosity (Richards et al., 2006), and that in itself
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could drive ecological success. The confounding with ploidy level makes it difficult to pinpoint the evolutionary
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consequences of the sexual-asexual transition and to test how the mode of reproduction affects selection for
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plasticity and a GPG strategy. An alternative way to gain insight into the role of GPG selection in geographic
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parthenogenesis and asexual range expansion is to compare asexual lineages that have successfully migrated
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far from their place of origin to asexual genotypes that have not migrated far. This approach avoids the ploidy
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confound and can test the hypothesis that a GPG strategy facilitates range expansion.
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In this study we compare asexual lineages of Taraxacum officinale (common dandelions, Kirschner and
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Štěpánek, 2011) sampled along a geographic transect of historical range expansion. T. officinale has a well-
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described pattern of geographic parthenogenesis in Europe: sexual plants are self-incompatible diploids that
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occur in south-central Europe, whereas triploid asexuals co-occur with sexuals in south-central Europe but
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extend much further northwards into Scandinavia (Mogie and Ford, 1988; Menken, Smit and Nijs, 1995; van
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Dijk, 2003). Asexuality in T. officinale is through meiotic diplosporous apomixis, which is characterized by clonal
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seed production from unreduced and unfertilized egg cells (Koltunow, 1993). New apomicts, here referred to
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as asexual lineages, are derived from diploid sexual mother plants in hybridizations with triploid or tetraploid
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pollen donors in the area where asexuals and sexuals co-occur (Tas and Van Dijk, 1999; van Dijk, 2003). The
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incipience of novel lineages through hybridizations with sexual ancestors is an ongoing process that contributes
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to high clonal diversity; a similar phenomenon is also observed in other sexual-asexual species complexes
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(Dybdahl and Lively, 1995; Delmotte et al., 2001; Vorburger, Lancaster and Sunnucks, 2003). Because asexual T.
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officinale lineages originate in southern areas, current northern asexual populations consist of genotypes that
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migrated northward and successfully faced spatial and temporal environmental variation during the process.
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Southern asexual populations, in contrast, include genotypes that did not migrate far and in addition may
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include many recently formed asexual lineages. The hypothesis that we test in this study is that northern
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populations are enriched for GPGs, which would reflect a selective advantage of a GPG-strategy during
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northward range expansion.
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To test this hypothesis we performed a factorial greenhouse experiment using only asexual lineages sampled
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along a latitudinal transect from west-central Europe to mid-Sweden (Fig. 1). The southern part of this asexual
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transect is close to the area of sexual-asexual co-occurrence where new asexual lineages arise from sexual
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ancestors (Menken, Smit and Nijs, 1995; van Dijk, 2003). Performance of clonal offspring of these asexual
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lineages was evaluated in three experimental environments; drought, shade and optimal greenhouse
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conditions. We evaluated if northern asexual lineages show (1) lower variation in performance across the three
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test environments and (2) higher geometric mean performance compared to southern asexual lineages,
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consistent with an enrichment of GPGs in northern populations.
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Materials and methods
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Plant material
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T. officinale populations are highly polyclonal in western and central Europe, presumably driven by a high rate
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of production of new asexuals in mixed populations (Menken, Smit and Nijs, 1995). Also populations in
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northern Europe are diverse clonal assemblages (Van Der Hulst et al., 2000). This was confirmed by ongoing
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work in our lab; using AFLP data in asexual dandelion populations we observed an average of ~13 different
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asexual lineages in random samples of 16 plants per location (range 11 – 15 lineages, n = 10 different
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locations, unpublished data) and the diversity was only slightly reduced in Swedish compared to Dutch and
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German populations.
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Within the North-West European distribution range of asexual Taraxacum officinale three regions were
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selected (see Fig. 1): close to the area where sexual and asexual plants co-occur in central Europe (hereafter
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referred to as the ‘south’ region of the transect), towards the asexuals’ northern distribution edge (‘north’) and
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a region in between (‘middle’). Seeds were collected in spring 2011 from four locations within each region. At
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each location plants were sampled randomly from different fields in an effort to obtain unbiased sampling of
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the locations’ genetic diversity. In this study we included four lineages per location, each sampled from
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different grasslands (pastures or fallows) within an approximate 5-10 km radius. Field-collected seeds were
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propagated in a common greenhouse environment for one generation in order to remove possible maternal
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effects associated with the original field environments. Ploidy level was determined flow-cytometrically for
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each lineage in leaf tissue by comparing nuclear DNA content of the unknown plants to a diploid reference
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plant using the Partec Ploidy Analyser (Tas and Van Dijk, 1999). All plants were confirmed to be triploid, and
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thus apomictic (Van Dijk et al., 1999; Van Der Hulst et al., 2000).
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Experimental design and plant growth
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Seeds from 48 lineages (three transect regions x four locations per region x four lineages per location) were
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surface sterilized with a 5 minutes wash in 0.5% sodium hypochlorite including 0.05% Tween®20 (Sigma
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Aldrich, the Netherlands) followed by two washing steps using DEMI water after which they germinated on
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0.8% agar plates for 8 days (16h light/20°C: 8h dark/15°C). The seedlings were transplanted individually to 0.4 L
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pots containing a soil mixture of 50% potting soil and 50% 0.4-0.8 µm sand, and grown under greenhouse
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conditions (16h light/21°C: 8h dark/16°C, 60% relative humidity on average, daylight maintained at a minimum
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of 225 µ.mol/m2/s PAR using Son-T GP 600W lights (Philips)).
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The experiment followed a blocked split-plot design with 4 replicated blocks. Within each block different
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treatments (shade, drought stress and optimal conditions; main plot level) were applied to trays with 48 plants
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(one plant per lineage; subplot level, randomized within each tray). These specific environmental treatments
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were chosen because variation in light and drought conditions have previously been implicated as relevant to
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performance in natural Taraxacum habitats (de Kovel and de Jong, 1999; Brock and Galen, 2005; Brock, Weinig
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and Galen, 2005; Molina-Montenegro et al., 2013). We included an environment that is optimal for growth
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because this enables identification of a typical ‘master-of-some’ strategy among the genotypes used (Richards
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et al., 2006). We point out that the Richards et al. (2006) model that distinguishes the ‘master-of-some’ from
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‘jack-of-all-trades’ strategies emphasizes differences in performance along a gradient of single environmental
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factors. Our experimental design, in contrast, takes a more general approach by testing performance in
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different stressful environments rather than at multiple levels of the same stress factor. This more general
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approach is consistent with the verbal model of general purpose genotypes proposed by Lynch (1984), but links
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directly to Richards et al.’s (2006) framework only to the extent that jack-of-all trades / GPG – genotypes show
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robust fitness in response to multiple different stress environments. During the experimental treatments all
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plants received the same amount of half-strength Hoagland nutrient solution (once a week 4L per tray of 48
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plants) and plants in the ‘shade’ and ‘optimal’ blocks were given additional water up to 3 times per week as
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required.
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The shade treatment was started 11 days after transplanting, by placing a green light filter-covered frame over
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the ‘shade’ main plot trays (Lee 122 Fern Green filter, Stage Supplies). The drought treatment was started 17
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days after transplanting, by withholding water from the ‘drought’ main plot trays. Water was withheld for 6 or
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7 days until about half of the plants started to show signs of wilting. In total there were three drought episodes
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during the experiment. During the entire experiment, plants in the drought environment received the same
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amount of Hoagland nutrient solution as plants in the other treatments. Plants were harvested 39 days after
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transplanting and final biomass was used as a proxy for plant performance. Harvesting the experiment at a
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relatively young vegetative stage allowed us to include a larger number of lineages and replicates in the
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experiment and also avoided variation due to between-lineage differences in vernalization requirements. We
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measured the length of the longest leaf and afterwards shoot and root tissue were separated and dried for 8-9
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days at 70 degrees to determine dry weight.
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Data analysis
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We used a mixed modeling split-plot analysis to test whether plants from the three transect regions (south,
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middle, north) differed in biomass and/or in their response to the environmental treatments. Transect region
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and treatment were considered as fixed factors. Lineage was modeled as a random factor, which was
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hierarchically nested within location and within transect region. Significance of the random factors (block,
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lineage, location, and their interactions with treatment) was tested using likelihood ratio chi square tests, by
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dropping individual random factors from the model and evaluating the difference in -2 log likelihood values
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between full and reduced models against a chi square distribution at one degree of freedom. Two lineages
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germinated poorly (one from the south and one from the north transect region) and some seedlings from
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different lineages failed to establish well after transplanting; these plants were excluded from the analysis.
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Therefore the analysis included a total of 529 plants (173 ‘south’, 182 ‘middle’ and 174 ‘north’ plants). Biomass
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data were ln-transformed and root:shoot biomass ratio was square root-transformed in order to meet the
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assumption of normality of residuals.
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To describe lineage performance across the three test environments we first extracted lineage mean values for
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total biomass in each test environment as least squares means from the split-plot model described above
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(treating all factors as fixed for this purpose) and back-transformed these to normal scale. Then we calculated,
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for each lineage, the geometric mean and the standard deviation of its three line mean values from the
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different test environments. One-way ANOVAs were used to test for effects of transect region (south, middle,
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north) on the geometric mean and standard deviation scores. In these ANOVAs we also tested if south, middle
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and north regions show different levels of variation in the geometric mean and standard deviation scores, using
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Levene’s tests for homogeneity of variances. All analyses were performed in SAS 9.2 software (SAS Institute
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Inc., Cary, NC, USA), using the PROC MIXED and PROC GLM packages.
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Results
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Biomass was affected by treatment and lineage (Table 1). No significant difference in biomass was observed
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between lineages from the three transect regions (south, middle, and north) and the region x treatment
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interaction also was not significant at the P=0.05 level. The non-significant region effect might be due to limited
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statistical power in the random-effects analysis. We therefore repeated the analysis with lineage and location
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as fixed factors and also in this model no significant difference between regions was detected (data not
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shown). This lends confidence to the interpretation that no large overall differences in performance exist
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between plants from different transect regions. However, the fixed-effect model showed a significant region x
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treatment effect (F4,382=4.18, P=0.0025), which indicates that for the set of lineages included in the experiment
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the three regions responded differently to the treatments. The southern lineages in this experiment showed
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relatively high biomass in the optimal condition. In contrast, although regional differences were generally
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smaller under stressful (drought and shade) conditions, southern lineages showed relatively low biomass under
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drought stress compared to the lineages of the other transect regions (Fig. 2).
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Northern lineages showed more even performance across the three test environments compared to southern
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lineages: variation in biomass, quantified for each lineage as the standard deviation of the line mean scores in
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the three test environments, significantly decreased along the geographic transect from south to north (Fig. 3a;
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F2,43=6.1, P=0.005). Geometric mean performance across the three test environments did not differ significantly
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between the regions (Fig. 3b; F2,43=0.57, P=0.57). Thus, while the average performance across the experimental
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environments remains comparable along the transect, southern lineages differ from northern lineages in how
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this average is achieved: southern lineages tend to be good performers in the optimal environment, whereas
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northern lineages achieve a comparable average via more even performance in both the optimal and stressful
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environments. Within the southern region, there were large differences in geometric mean performance
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between individual lineages, including lineages that showed relatively high performance across all
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environments but also lineages with consistent low performance across environments (Figs. 2 and 3b). In
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contrast, between-lineage differences within the northern region were significantly smaller (Levene’s test for
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homogeneity of variances between transect regions in geometric mean performance scores: P=0.037).
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Plants developed longer leaves in the shade environment compared to control plants (treatment effect:
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F1,3=356, P=< 0.001; Fig. 4a) and in the drought environment the root:shoot ratio was increased (treatment
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effect: F1,3=23.9, P=0.02; Fig. 4b). However, no significant difference was observed between the three transect
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regions in the treatment response of these traits (treatment x region interaction for leaf response to shading:
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F2,9=0.1, P=0.92; for root:shoot response to drought: F2,9=0.8, P=0.49). This interaction was also not significant
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for either trait when lineage was modeled as a fixed factor (data not shown).
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Discussion
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We found higher variation in performance between the experimental environments in southern asexual
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lineages, collected close to the area where sexuals and asexuals co-occur and where asexuals arise from sexual
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ancestors, compared to northern asexual lineages that were collected far away from this area of sexual-asexual
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co-occurrence. Southern lineages showed a relatively high biomass in the optimal environment, whereas
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northern lineages showed a more even performance profile across all environments. Thus, there appears to be
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a shift from south to north during clonal migration in the way in which phenotypic plasticity is important:
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southern lineages seem to follow the master-of-some strategy which emphasizes the ability to rapidly take
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advantage of a good environment (Richards et al., 2006), while northern lineages seem to be selected for a
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jack-of-all-trades (or GPG) strategy that emphasizes robust performance across environments (Richards et al.,
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2006). These results suggest that phenotypic plasticity is important for T. officinale along the entire transect,
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but clonal migration from core to northern range edge selects for lineages with a robust performance and thus
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general-purpose genotypes.
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The observed high performance of southern lineages under optimal conditions but below-average performance
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under drought stress suggests that GPG selection during range expansion comes at the expense of suboptimal
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performance under optimal conditions. This interpretation fits well with theory (Richards et al., 2006), but we
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point out that the observed within-environment differences in performance between southern and northern
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lineages were not large and statistically significant only in fixed-effects models (Fig. 2a). This precludes
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extrapolation of test results beyond the genotypes included in the experiment, and thus only cautious
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interpretation is possible. One alternative possibility for the observed higher between-environment variation in
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performance of southern lineages could be that the optimal greenhouse environment is more similar to
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southern than to northern habitats, contributing to relatively high performance of southern lineages in this test
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environment. In that case, the observed pattern along the transect could be due to a loss of high-performance
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genotypes in rare optimal environments rather than due to active selection for GPGs. While our data are more
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consistent with active selection for robust performance also under stressful environments, more
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comprehensive experiments would be desirable that include more treatment levels (Richards et al., 2006;
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Muth and Pigliucci, 2007) and more test environments that are ecologically relevant to the transect.
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In contrast to predictions under GPG selection (Vorburger, Sunnucks and Ward, 2003; Richards et al., 2006) we
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found no significant difference in geometric mean performance across environments between southern and
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northern lineages. Thus northern lineages are characterized by a more even performance across different
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environments, but not by a higher overall performance compared to the southern lineages. Clonal age might
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have contributed to this pattern: if, on average, northern lineages are older compared to southern lineages,
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then northern lineages may suffer more from accumulation of deleterious mutations and this may have a
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negative effect on average performance (Mogie and Ford, 1988; Lynch et al., 1993; Sniegowski et al., 2000; van
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Dijk, 2003; Ossowski et al., 2010). Such an effect of mutation accumulation would act independently from
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selection for GPGs along the transect. The net effect could be unaffected or even reduced geometric mean
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performance across environments along the transect parallel to increased selection for GPGs. Clonal age might
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also affect performance in another way that is correlated with the transect. It is known that newly formed
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apomicts can have unstable genomes, affecting their performance and survival (Comai, 2005; Horandl, 2006;
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Verhoeven, Van Dijk and Biere, 2010). New asexual lineages arise in the area were sexual and asexual plants co-
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occur and therefore young asexual lineages are likely more frequent in the southern part than in the northern
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part of our transect. Interestingly, within experimental environments we observed highest between-lineage
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variation in performance in the southern plants, including some strikingly poor-performing lineages (Fig. 2b).
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This could be a reflection of some unstable young asexual lineages that have not yet disappeared from the
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population by selection. We propose that in our experimental setup, where average clonal age and mutation
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load might be different between regions along the transect, the key characteristic for GPGs is the low variation
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in performance across environments. Both genome instabilities and mutation accumulation may differ along
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the transect and may have an impact on the overall performance estimates, however it is important to note
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that these factors are not expected to affect the variation in performance between-environments and
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therefore do not affect the GPG interpretation and loss of the master-of-some strategy.
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Maintaining a relatively even performance in different environments can be mediated by phenotypic plasticity
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in underlying functional traits. For instance, adaptive modulation of root:shoot ratio in response to drought
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may reduce the negative performance effects of drought stress by investing in tissue that captures water
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(roots) at the expense of tissue that evaporates water (leaves) (Sharp and Davies, 1979; Brock and Galen,
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2005). Plasticity in drought response has been shown to differ between native and invasive Taraxacum species
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(Quiroz et al., 2009; Molina-Montenegro et al., 2011). Similarly, asexual T. officinale lineages develop longer
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leaves than their sexual conspecifics in response to shade, suggesting higher adaptive phenotypic plasticity as
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longer leaves can have a performance benefit under competition for light (de Kovel and de Jong, 1999). In this
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experiment we measured root:shoot ratio and leaf length and confirmed that root:shoot ratio is increased
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under drought and leaf length is increased under shade conditions. However, these stress responses did not
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differ significantly between regions (Fig. 4). Therefore the more even performance of northern lineages across
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environments does not seem to be explained by adaptive phenotypic plasticity in these specific functional
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traits.
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Our study points towards the relevance of a GPG strategy for successful range expansion, however, studies of
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invasive T. officinale populations in America have emphasized the presence of habitat specialist asexual
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lineages in invasive populations (Solbrig and Simpson, 1977; Vavrek, McGraw and Yang, 1996; Vellend,
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Drummond and Muir, 2009; Molina-Montenegro et al., 2011; McLeod, Scascitelli and Vellend, 2012; Molina-
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Montenegro et al., 2013). This is an intriguing difference with our results, because a GPG strategy would also
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be predicted to have a selective advantage for asexual T. officinale in recently invaded areas. While the
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difference may be partly due to differences between studies in interpretation and analysis (for instance, also in
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our study some individual northern lineages could be labeled as specialists; see Fig. 2d), invasive American and
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range expanding North European populations also differ in important aspects. American asexual populations
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show reduced clonal diversity (due to limited influx of new lineages) and have established over a much reduced
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time scale compared to North European populations (Solbrig and Simpson, 1974; Lyman and Ellstrand, 1984;
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Collier and Rogstad, 2004). One speculative explanation is therefore that GPG selection acts also in America but
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limited time and genetic variation has not resulted in the same response to selection as observed in Europe.
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The idea that GPG-selection contributes to geographic parthenogenesis rests on two underlying hypotheses:
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first, that clonal selection favours a GPG strategy compared to sexual reproduction, and second, that a GPG-
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strategy facilitates range expansion and colonization of a new habitat. By exploiting geographic variation
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within asexual lineages along a transect of historical range expansion this study showed that a GPG strategy is
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associated with successful asexual range expansion in a geographic parthenogenesis system, in a way that
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avoided the confounding effect of ploidy differences that is typical of sexual-asexual comparisons in geographic
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parthenogenesis studies. This study thus confirmed the second hypothesis that a GPG-strategy facilitates range
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expansion. However, we did not test the first hypothesis that clonal reproduction selects for a GPG strategy
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more than sexual reproduction does. Testing this hypothesis requires sexual-asexual comparisons. However,
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our results may provide some indirect evidence also for this first hypothesis, namely to the extent that
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southern lineages in our transect represent a recently derived sample of the sexual population from which they
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arise, with traits similar to the ancestral sexual population. While this might be true, explicit sexual-asexual
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comparisons in this system need to be included for comprehensive tests of the role of GPG selection in
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geographic parthenogenesis (de Kovel and de Jong, 1999). Our study highlights the importance of phenotypic
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plasticity for asexual dandelions throughout their entire distribution in Europe and suggests that the process of
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northward migration selects for asexual lineages that have a GPG strategy.
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Acknowledgements
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We thank Veronica Preite for providing seeds, Gregor Disveld, Slavica Milanovic-Ivanovic and Rutger Wilschut
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for help during the experiment. Furthermore we thank Arjen Biere, the associate editor and two anonymous
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reviewers for helpful comments on the manuscript. Funding for this study was provided by the Netherlands
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Organisation for Scientific Research (NWO-ALW grant 864.10.008 to KJFV). This is publication 5710 of the
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Netherlands Institute of Ecology (NIOO-KNAW).
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Data accessibility
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Data deposited in the DRYAD repository: http://doi.org/10.5061/dryad.3fr47 (Oplaat and Verhoeven,
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2014)
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References
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Table and Figures
Table 1. Mixed model analysis of plant biomass. Significance of random effects was evaluated using likelihood
ratio tests, comparing the full model to a nested model without that specific random effect
F value (df num, df den)
Likelihood Ratio χ2 (df)
P value
Fixed effects
Transect region
Treatment
Transect region x Treatment
0.14 (2, 9)
0.870
27.05 (2, 6)
0.001
2.29 (4,18)
0.111
Random effects
Block
0.2 (1)
0.655
Location*
0.8 (1)
0.371
Lineage†
48.1 (1)
<0.0001
Block x Treatment
53.3 (1)
<0.0001
Location* x Treatment
2.0 (1)
0.157
Lineage† x Treatment
0.3 (1)
0.584
* nested within regions
† hierarchically nested within locations and regions
22
Fig. 1. Collection sites of the Taraxacum officinale lineages used in the study. Each dot represents a sampling
location from which four lineages were included. Dashed lines are estimated distribution borders of sexual
diploid Taraxacum officinale based on Menken et al. (1995). South of these distribution borders there is a cooccurrence of sexual and asexual dandelions and north of these borders only asexual lineages occur.
23
Fig. 2. Total biomass (g) across the different test environments. (a) Average biomass by region and treatment.
The average values of the lineage means per region are shown with corresponding SE. Letters indicate
statistical differences between regions in post-hoc Tukey comparisons from fixed-effects models of individual
plant biomass (see main text) fitted separately per treatment. (b-d) The response of individual lineages to the
different test environments, plotted separately for southern lineages (b), mid-latitude lineages (c) and northern
lineages (d). Lineage means are back-transformed LS means derived from a fixed-effect linear model.
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Fig. 3. Summary statistics of lineage performance across the three test environments. (a) The standard
deviation of line mean biomass scores (grey dots) across the three environments, calculated for each lineage
separately, differed significantly between regions (P=0.0005). (b) The geometric mean biomass (grey dots)
across the three environments per lineage was not significantly different between the regions (P=0.57). Black
rectangles represent the average per region.
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Fig. 4. Regional responses to shading and to drought stress. (a) Leaf elongation in response to shading. Average
LS-mean of the length of the longest leaf (cm) under optimal and shade conditions per transect region. (b)
Root:shoot ratio in response to drought stress. Average LS mean of the root:shoot ratio under optimal and
drought conditions per region. Error bars represent the standard error. In both cases the treatment response
was significant (leaf elongation P= <0.001 and root:shoot ratio P=0.02), but regions did not respond differently
to treatments (treatment x region interaction for leaf elongation: P=0.92; for root:shoot ratio: P= 0.49).
26